IDEAS home Printed from https://ideas.repec.org/a/spr/jqecon/v19y2021i1d10.1007_s40953-021-00278-4.html
   My bibliography  Save this article

Estimation of Random Components and Prediction in One and Two-Way Error Component Regression Models

Author

Listed:
  • Subhash C. Sharma

    (Southern Illinois University Carbondale)

  • Anil K. Bera

    (University of Illinois at Urbana-Champaign)

Abstract

Since one of the main objectives of panel data analysis is to uncover individual and/or time effects, the estimation of these random components is very important. Estimation of individual and time components will also help in predicting the future values of the dependent variable, which has received some attention in the literature. Following the stochastic frontier literature, our contention is that estimation of these random components is akin to the estimation of “firm-specific” efficiency. Thus, considering the conditional distributions of the random components and using the conditional mean and variance, we provide both point and interval estimates of the individual and time effects in the one and two-way error component models. Using these standard errors one can also test the significance of random components. Equations for predictions are also provided for these models. Finally, all our theoretical results are illustrated with an empirical application.

Suggested Citation

  • Subhash C. Sharma & Anil K. Bera, 2021. "Estimation of Random Components and Prediction in One and Two-Way Error Component Regression Models," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(1), pages 419-441, December.
    • Handle: RePEc:spr:jqecon:v:19:y:2021:i:1:d:10.1007_s40953-021-00278-4
    DOI: 10.1007/s40953-021-00278-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40953-021-00278-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s40953-021-00278-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Anil Bera & Subhash Sharma, 1999. "Estimating Production Uncertainty in Stochastic Frontier Production Function Models," Journal of Productivity Analysis, Springer, vol. 12(3), pages 187-210, November.
    2. Swamy, P A V B & Arora, S S, 1972. "The Exact Finite Sample Properties of the Estimators of Coefficients in the Error Components Regression Models," Econometrica, Econometric Society, vol. 40(2), pages 261-275, March.
    3. Badi H. Baltagi & Bernard Fingleton & Alain Pirotte, 2014. "Estimating and Forecasting with a Dynamic Spatial Panel Data Model," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(1), pages 112-138, February.
    4. Fuller, Wayne A. & Battese, George E., 1974. "Estimation of linear models with crossed-error structure," Journal of Econometrics, Elsevier, vol. 2(1), pages 67-78, May.
    5. Taub, Allan J., 1979. "Prediction in the context of the variance-components model," Journal of Econometrics, Elsevier, vol. 10(1), pages 103-107, April.
    6. Bera, Anil K. & Doğan, Osman & Taşpınar, Süleyman & Leiluo, Yufan, 2019. "Robust LM tests for spatial dynamic panel data models," Regional Science and Urban Economics, Elsevier, vol. 76(C), pages 47-66.
    7. Jondrow, James & Knox Lovell, C. A. & Materov, Ivan S. & Schmidt, Peter, 1982. "On the estimation of technical inefficiency in the stochastic frontier production function model," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 233-238, August.
    8. Jushan Bai, 2013. "Fixed‐Effects Dynamic Panel Models, a Factor Analytical Method," Econometrica, Econometric Society, vol. 81(1), pages 285-314, January.
    9. Baltagi, Badi H. & Griffin, James M., 1983. "Gasoline demand in the OECD : An application of pooling and testing procedures," European Economic Review, Elsevier, vol. 22(2), pages 117-137, July.
    10. Amemiya, Takeshi, 1971. "The Estimation of the Variances in a Variance-Components Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 12(1), pages 1-13, February.
    11. Wallace, T D & Hussain, Ashiq, 1969. "The Use of Error Components Models in Combining Cross Section with Time Series Data," Econometrica, Econometric Society, vol. 37(1), pages 55-72, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yong Bao & Aman Ullah, 2021. "The Special Issue in Honor of Anirudh Lal Nagar: An Introduction," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(1), pages 1-8, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Marius C. O. Amba, 2021. "Simultaneous Equations with Three Way Error Components," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(3), pages 583-596, September.
    2. Dong Kim, 2012. "What is an oil shock? Panel data evidence," Empirical Economics, Springer, vol. 43(1), pages 121-143, August.
    3. Badi H. Baltagi, 1987. "On Estimating from a More General Time-Series Cum Cross-Section Data Structure," The American Economist, Sage Publications, vol. 31(2), pages 69-71, October.
    4. Malekzadeh, Ahad & Esmaeli-Ayan, Asghar, 2021. "An exact method for testing equality of several groups in panel data models," Statistics & Probability Letters, Elsevier, vol. 177(C).
    5. Laura Magazzini & Giorgio Calzolari, 2010. "Negative variance estimates in panel data models," Working Papers 15/2010, University of Verona, Department of Economics.
    6. Hamid Beladi & Nicholas S. P. Tay & Reza Oladi, 2011. "On Competition for Listings," Working Papers 0003, College of Business, University of Texas at San Antonio.
    7. Issler, João Victor & Lima, Luiz Renato, 2009. "A panel data approach to economic forecasting: The bias-corrected average forecast," Journal of Econometrics, Elsevier, vol. 152(2), pages 153-164, October.
    8. Ahn, Seung C. & Lee, Young H. & Schmidt, Peter, 2013. "Panel data models with multiple time-varying individual effects," Journal of Econometrics, Elsevier, vol. 174(1), pages 1-14.
    9. Baltagi, Badi H. & Blien, Uwe, 1998. "The German wage curve: evidence from the IAB employment sample," Economics Letters, Elsevier, vol. 61(2), pages 135-142, November.
    10. repec:jss:jstsof:27:i02 is not listed on IDEAS
    11. John Merrifield & Yong Bao, 2007. "Residential Property Taxation: Is Periodic Reassessment worth it?," Working Papers 0003, College of Business, University of Texas at San Antonio.
    12. Jayasooriya, Sujith, 2021. "Impact of Agricultural Factors on Carbon Footprints for GHG Emission Policies in Asia," MPRA Paper 109790, University Library of Munich, Germany.
    13. Baltagi, Badi H. & Pirotte, Alain, 2010. "Panel data inference under spatial dependence," Economic Modelling, Elsevier, vol. 27(6), pages 1368-1381, November.
    14. Marcel die Dama & Boniface ngah Epo & Galex syrie Soh, 2013. "Developing a two way error component estimation model with disturbances following a special autoregressive (4) for quarterly data," Economics Bulletin, AccessEcon, vol. 33(1), pages 625-634.
    15. Esfandiar Maasoumi & Almas Heshmati & Inhee Lee, 2021. "Green innovations and patenting renewable energy technologies," Empirical Economics, Springer, vol. 60(1), pages 513-538, January.
    16. Baltagi, Badi H., 2013. "Panel Data Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 995-1024, Elsevier.
    17. Baltagi, Badi H. & Pirotte, Alain, 2014. "Prediction in a spatial nested error components panel data model," International Journal of Forecasting, Elsevier, vol. 30(3), pages 407-414.
    18. H. Baltagi, Badi & Heun Song, Seuck & Cheol Jung, Byoung, 2001. "The unbalanced nested error component regression model," Journal of Econometrics, Elsevier, vol. 101(2), pages 357-381, April.
    19. Subal C. Kumbhakar & Christopher F. Parmeter & Valentin Zelenyuk, 2022. "Stochastic Frontier Analysis: Foundations and Advances I," Springer Books, in: Subhash C. Ray & Robert G. Chambers & Subal C. Kumbhakar (ed.), Handbook of Production Economics, chapter 8, pages 331-370, Springer.
    20. repec:lan:wpaper:1043 is not listed on IDEAS
    21. Getu Hailu & B. James Deaton, 2016. "Agglomeration Effects in Ontario’s Dairy Farming," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 98(4), pages 1055-1073.
    22. Yangseon Kim & Peter Schmidt, 2000. "A Review and Empirical Comparison of Bayesian and Classical Approaches to Inference on Efficiency Levels in Stochastic Frontier Models with Panel Data," Journal of Productivity Analysis, Springer, vol. 14(2), pages 91-118, September.

    More about this item

    Keywords

    Panel data models; One-way random components model; Two-way random components model; Estimation; Prediction;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:jqecon:v:19:y:2021:i:1:d:10.1007_s40953-021-00278-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.